Quick Path to Train, Score, and Operationalize a Machine Learning Project

ABSTRACT

Automatically detecting and anticipating that an additional machine learning experiment may be needed. A method includes after successfully running a first experiment workflow, automatically prompting a user that an additional experiment workflow may be needed based on specific criteria associated with the first experiment workflow. The method further includes receiving input from the user confirming the additional experiment workflow. As a result of receiving input from the user confirming the additional experiment workflow, the method further includes the system automatically reconfiguring the first experiment workflow, including automatically identifying all necessary modules for the additional experiment workflow and connecting them properly to perform the intended second experiment workflow. The method further includes displaying to the user the first experimental workflow transitioning from the first experiment workflow to the additional experiment workflow.

BACKGROUND Background and Relevant Art

Machine learning systems, such as Azure Machine Learning available fromMicrosoft Corporation of Redmond, Wash., allow data scientists to createa predictive model and put it into production, such as into a webservice that allows incoming data to be applied to the predictive model.In particular, a training experiment may be performed by applying testdata to the predictive model to train the predictive model. Informationgathered from the training experiment may be used to create a scoringexperiment that allows the trained predictive model to be used with realworld and real time data to perform predictive functionality on the realworld and real time data. This scoring experiment can be used as thebasis for implementing a web service. However, the process oftransforming a machine learning training experiment into a scoringexperiment, and subsequently into a web service requires a number ofdistinct steps that may not be intuitive to users. Thus, a technicalproblem may exist where it may be useful to simplify the process oftransforming training experiments into scoring experiments.

The subject matter claimed herein is not limited to embodiments thatsolve any disadvantages or that operate only in environments such asthose described above. Rather, this background is only provided toillustrate one exemplary technology area where some embodimentsdescribed herein may be practiced.

BRIEF SUMMARY

One embodiment includes a system for automatically detecting andanticipating that an additional machine learning experiment may beneeded. The system includes one or more processors and one or morecomputer-readable media. The one or more computer-readable media includecomputer-executable instructions that when executed by at least one ofthe one or more processors cause the system to perform the followingmethod. The method includes after successfully running a firstexperiment workflow, automatically prompting a user that an additionalexperiment workflow may be needed based on specific criteria associatedwith the first experiment workflow. The method further includesreceiving input from the user confirming the additional experimentworkflow. As a result of receiving input from the user confirming theadditional experiment workflow, the method further includes the systemautomatically reconfiguring the first experiment workflow, includingautomatically identifying all necessary modules for the additionalexperiment workflow and connecting them properly to perform the intendedsecond experiment workflow. The method further includes displaying tothe user the first experimental workflow transitioning from the firstexperiment workflow to the additional experiment workflow.

Another embodiment may be practiced in a computing environment andincludes a method of automatically detecting and anticipating that anadditional machine learning experiment may be needed. The methodincludes after successfully running a first experiment workflow,automatically prompting a user that an additional experiment workflowmay be needed based on specific criteria associated with the firstexperiment workflow. The method further includes receiving input fromthe user confirming the additional experiment workflow. As a result ofreceiving input from the user confirming the additional experimentworkflow the method further includes the system automaticallyreconfiguring the first experiment workflow, including automaticallyidentifying all necessary modules for the additional experiment workflowand connecting them properly to perform the intended second experimentworkflow. The method further includes displaying to the user the firstexperimental workflow transitioning from the first experiment workflowto the additional experiment workflow.

Another embodiment includes a system for creating a scoring experimentfrom a training experiment. The system includes one or more processorsand one or more computer-readable media. The one or morecomputer-readable media include computer-executable instructions thatwhen executed by at least one of the one or more processors cause thesystem to perform the following method. The method includes identifyingelements of a training experiment. The method further includesidentifying consolidation and elimination of elements in the trainingexperiment for a scoring experiment. The method further includesdetermining that the user wishes to have a scoring experiment createdfrom the training experiment. The method further includes consolidatingand eliminating the identified elements create the scoring experiment.

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used as an aid in determining the scope of the claimed subjectmatter.

Additional features and advantages will be set forth in the descriptionwhich follows, and in part will be obvious from the description, or maybe learned by the practice of the teachings herein. Features andadvantages of the invention may be realized and obtained by means of theinstruments and combinations particularly pointed out in the appendedclaims. Features of the present invention will become more fullyapparent from the following description and appended claims, or may belearned by the practice of the invention as set forth hereinafter.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to describe the manner in which the above-recited and otheradvantages and features can be obtained, a more particular descriptionof the subject matter briefly described above will be rendered byreference to specific embodiments which are illustrated in the appendeddrawings. Understanding that these drawings depict only typicalembodiments and are not therefore to be considered to be limiting inscope, embodiments will be described and explained with additionalspecificity and detail through the use of the accompanying drawings inwhich:

FIG. 1A illustrates a user interface with a workflow representing atraining experiment;

FIG. 1B illustrates user interface showing the training experimentrunning;

FIG. 1C illustrates the user interface including a prompt to a user forthe creation of a scoring experiment;

FIG. 1D illustrates the user interface with the elimination of variousworkflow elements from the training experiment in a scoring experiment;

FIG. 1E illustrates the user interface with the addition of web serviceinput and output nodes in the scoring experiment;

FIG. 1F illustrates FIG. 1F illustrates a screen capture of the userinterface showing an animation combining elements;

FIG. 1G illustrates a cue describing actions performed for a scoringexperiment;

FIG. 1H illustrates another cue describing actions performed for ascoring experiment;

FIG. 1I illustrates another cue describing actions performed for ascoring experiment;

FIG. 1J illustrates another cue describing actions performed for ascoring experiment;

FIG. 1K illustrates another cue describing actions performed for ascoring experiment;

FIG. 1L illustrates another cue prompting a user to publish a scoringexperiment;

FIG. 1M illustrates a user interface element that allows for togglingbetween a web service view and a simple workflow view;

FIG. 2 illustrates a block diagram of an example system;

FIG. 3 illustrates a method of creating a scoring experiment from atraining experiment; and

FIG. 4 illustrates a method of automatically detecting and anticipatingthat an additional machine learning experiment may be needed

DETAILED DESCRIPTION

Embodiments herein may implement a system for designing and/orimplementing a defined, linear workflow which handholds the user from atraining experiment, to a scoring experiment, to a web service, makingthe operationalization process simple and intuitive. Embodiments of thesystem may guide the user through the process of creating a web servicethrough a series of hints and clearly defined steps. Some embodimentscan analyze a user's training experiment and prune the trainingexperiment to create a scoring experiment, which is a shareable andconsolidated form of a trained version of the training experiment. Inthe example illustrated, the determination that a scoring experimentshould be created from a training experiment can be made based on thetraining experiment having a training module and an algorithm module inthe training experiment, which indicate that the user may wish to createa simplified scoring experiment from the training experiment.

By analyzing a user's training experiment and pruning the trainingexperiment to create a scoring experiment, a technical effect ofincreased user efficiency and/or increased user interaction performancecan be achieved. In particular, by consolidating training experimentelements into scoring experiment elements, and removing elements andremoving elements that are only needed for the training experiment, auser's effort in evaluating an experiment can be reduced as the user canmore efficiently evaluate elements on the screen. This can reduce theuser's mental efforts. Further, the elements are arranged on the screenmore efficiently for more efficient user interaction.

Additionally the system performance can be increased by eliminating theneed for much of the detailed user interaction previously required todefine scoring experiments from training experiments. Rather, by thesystem automatically determining how a scoring experiment should becreated from a training experiment, the user interaction can beeliminated, or at least drastically reduced. This user interaction istypically costly in terms of computing resources as processes areinterrupted and diverted to handle user interface interactions. Thus,rather than the system needing to divert resources to handle userinterface interrupts, the system can use those computing resources tomore quickly perform various machine learning, or other tasks. Thus, byeliminating certain costly operations, computing resources can be usedto perform other operations faster, thus improving the overallperformance of the system.

Once the consolidated form of the trained version of the trainingexperiment is completed correctly, i.e. the scoring experiment,publishing a web service API can be efficiently performed.

Thus, a user can draw a production workflow by dragging and droppingvarious modules, and with a few clicks, create a public REST API thatembeds custom logic and machine learning models. Embodiments canautomatically handle the deployment, including capacity provisioning,load balancing, auto-scaling and health monitoring, so that the userdoes not have to worry about deploying, scaling or monitoring the newlycreated web service. Enterprise and mobile applications can leveragecloud hosted intelligence at scale by using such web services.

Referring now to FIGS. 1A-1L, a functional example is illustrated. FIG.1A illustrates a graph 102 that represents a training experiment 100. Inparticular, the training experiment 100 may be an experiment to train analgorithm to predict approximate income based on input data.

FIG. 1A illustrates an algorithm module 104. The algorithm module 104includes a machine learning algorithm, which once trained, can predictan individual's income based on certain data about that individualprovided to the algorithm module 104.

The graph 102 illustrated in FIG. 1A may be constructed by a userdragging and dropping modules onto a canvas for the training experiment100. The following will now describe various modules and inputs used inthe training experiment.

FIG. 1A illustrates Adult Census Income input data 106. This particulardata is only for example purposes and it should be appreciated thatother data may be implemented in alternate examples. This input data 106includes various pieces of information correlated to income. Thus, forexample, a record in the input data 106 may include occupation, homeaddress, age, other demographic information, and income.

The information in the input data 106 may be fed into a missing valuesscrubber module 108, or other data transformation module(s). In thepresent example, the missing values scrubber module 108 checks for emptyor wrong values in a column and replaces them with user defined values.However, other data manipulation modules may be alternatively oradditionally included according to a user's preference.

FIG. 1A further illustrates a project columns module 110. The projectcolumns module 110 allows embodiments to select columns of data toinclude or exclude in the model. This may include excluding data columnsthat are irrelevant or that do not include any data.

FIG. 1A further illustrates a split module 112. The split module 112splits the data from the input data 106 into two different portions. Forexample, 60% of the input data may be used to train the predictivemodel, while the other 40% is used to test the trained predictive model.While a 60/40 split is illustrated here, it should be appreciated thatthe split may be user configurable and can be any ratio selected by theuser. However, in the illustrated example, 60% of the data from theinput data 106 is sent to the train module 114 and the score module 116where it will be used in conjunction with the algorithm module 104 toestimate income for an individual. Because the input data 106 includesincome data, the train module 114, the score module and the algorithmmodule 104 can use the other data in the input data 106, as operated onby the missing values scrubber 108, the project columns module 110 andthe split module 112 to train the predictive model. The output of thetrain module 114 is provided to the score module 116 which generates anincome prediction based on the input data 106.

FIG. 1A further illustrates that the other 40% of the input data 106 issent directly to a score module 118. The score module scores the datawithout the benefit of the train module 114 and the algorithm module104.

The results from the two score modules 116 and 118 can be compared by anevaluate module 120 to determine the accuracy of the predictive model.In this particular example, the evaluate module 120 includesfunctionality to determine that the scores from the score modules 116and 118 are sufficiently different to indicate that the training of thealgorithm module 104 has been effective.

While various modules are illustrated above, it should be appreciatedthat a virtually unlimited number of different types of datatransformation modules may be implemented. For example, in someembodiments, filter modules may be implemented. Such filter modules maybe, for example, one or more of an Apply Filter, FIR Filter, IIR Filter,Median Filter, Moving Average Filter, Threshold Filter, User DefinedFilter, etc. module. In some embodiments, manipulation modules may beimplemented. Such manipulation modules may be, for example, one or moreof an Add Columns, Add Rows, Group Categorical Values, Indicator Values,Join, Metadata Editor, Missing Values Scrubber, Project Columns, RemoveDuplicate Rows, etc. modules. In some embodiments, sample and splitmodules may be implemented. Such sample and split modules may be, forexample, one or more of a Partition and Sample, Split, etc. module. Insome embodiments, scale and reduce modules may be implemented. Suchscale and reduce modules may be, for example, one or more of an ApplyQuantization Function, Clip Values, Normalize Data, Quantize, etc.module.

As illustrated in FIG. 1B, the training experiment is run by the system.Running the training experiment creates the appropriate informationneeded for the train module to allow the algorithm in the algorithmmodule to be able to accurately predict income based on certaindemographic input data.

Once the training experiment has been run successfully, the system canprompt the user to automatically create a scoring experiment. In someembodiments, the system may prompt the user based on recognition by thesystem that the training experiment 100 is a training experiment. Thesystem can then recognize that a corresponding scoring experiment mayneed to be created. For example, in some embodiments, the system mayidentify that both an algorithm module (such as algorithm module 104)and a train module (such as train module 114) exist in an experiment(such as the training experiment 100). This may be an indication thatthe training experiment 100 is a training experiment for which acorresponding scoring experiment should be created. FIG. 1C illustratesa prompt 122 that asks the user if they would like to create a scoringexperiment. If the user clicks “yes”, then as illustrated in FIGS. 1Dthrough 1L, a scoring experiment 124 is created. The training experiment100 is maintained and can be re-accessed by the user as well.

The scoring experiment 124 is a consolidated version of the trainingexperiment 100. In particular, various elements of the trainingexperiment can be either combined in a graphical representation withother elements, or can be removed completely as they have no neededfunction in the scoring experiment 124. For example, FIG. 1D illustratesthe removal of the split module 112 as such a module is typically notused in a scoring experiment configured to operate on input data that isnot training data. Similarly, the corresponding score module 118 is alsoremoved. In some embodiments, removal of elements may be animated tohighlight removal to the user. For example, in some embodiments, thesplit module 112 and the score module 118 may be shown floating off ofthe screen.

FIG. 1E further illustrates the addition of a web service input node126. The web service input node 126 is used to obtain input data from aweb service. For example, the web service input node 126 may includeAPIs for interacting with various databases or for receiving manuallyentered input data. In the example illustrated, the web service inputnode 126 may be able to obtain demographic information aboutindividuals, such as age, home address, occupation, etc., that can beused to determine income for individuals when such input information isprovided to other modules in the scoring experiment.

FIG. 1E further illustrates the addition of a web service output node128. The web service output node 128 is able to provide interfaceelements that allow, in the illustrated example, predicted income to beprovided to a user interface. For example, the web service output modulemay include APIs that interface with various user interfaces to provideindications of income predictions.

While FIG. 1E illustrates the removal of certain modules (i.e. the splitmodule 112 and the score module 118) and the addition of certain modules(i.e. the web service input node 126 and the web service output node128), various modules from the training experiment can be consolidatedgraphically for the user. For example, FIG. 1F illustrates that thealgorithm module 104 and the train module 114 are consolidated into atrained model module 130. In some embodiments, this consolidation may beillustrated graphically using an animation to graphically identify forthe user the consolidation. For example, FIG. 1F shows a still captureof the algorithm module 104 and the train module 114 as they arefloating graphically into the trained module 130.

Embodiments may further provide various cues to the user about whatoperations have been performed to arrive at the scoring experiment 124.For example, FIG. 1G illustrates a cue 132 indicating that the scoringexperiment 124 has been created. The cue 132 also indicates to the userthat they can edit this experiment independently from the originaltraining experiment 100.

FIG. 1H illustrates a cue 134 indicating that the trained module 130 hasbeen added to the experiment 124 and that the trained module 130 can beused to create a scoring graph.

FIG. 1I illustrates a cue 136 indicating that the input and output nodes126 and 128 have been created. The cue 136 also indicates that thesenodes 126 and 128 were automatically placed in a determined position,but that the nodes can be moved by the user if the user finds thatmoving the nodes is appropriate.

FIG. 1J illustrates a cue 138 directing a user to user interfaceelements that allow the user to add additional inputs or outputs for usewith a web service deployment.

FIG. 1K illustrates a cue 140 directing a user to a web service userinterface element 146 that allows the user to toggle on or off a webservice view. This will be illustrated in more detail in conjunctionwith the description of FIG. 1M.

FIG. 1L illustrates a cue 142 directing a user to a user interfaceelement 144 that causes the scoring experiment to be published. Inparticular, interacting with the user interface element 144 can causethe scoring experiment 124 to be published to a web service.

As discussed above, embodiments may include functionality that allowsthe user to view the scoring experiment graph in the context of a webservice flow, or not in the context of a web service flow. FIGS. 1Ethrough 1L illustrate the scoring experiment graph 144 in the context ofa web service flow. FIG. 1M, in contrast, illustrates that a user hasselected, using the user interface element 146, to show the scoringexperiment graph 144′ not in the context of a web service flow. Inparticular, while the graph 144′ still includes the web service inputnode 126 and web service output node 128, the graph shows these twonodes 126 and 128 as not connected to the graph 144′. Thus, the exampleof FIG. 1M illustrates a scoring experiment graph 144′ that is not inthe context of a web service.

FIG. 2 illustrates a block diagram of one example system includingtechnical means to achieve experiment consolidation. FIG. 2 illustratesinput 202. The input 202 may be an experiment, such as a trainingexperiment. The input 202 is provided to a new experiment detectionmodule 204. The new experiment detection module 204 can determine if anew experiment could be created from the experiment in the input 202.This can be determined by characteristics of the experiment in the input202. For example, embodiments may be able to determine that theexperiment in the input 202 has a train module and an algorithm module,and this may be an indicator that the experiment is a trainingexperiment for which a scoring experiment could be created.

When it is determined that a new experiment could be created, theprevious experiment in the input 202 can be provided to a consolidationmodule 206. The consolidation module 206 can provide selectable elementsto a user prompting the user that the experiment in the input 202 couldbe used to create a new experiment and requesting user input 208indicating that a new experiment should be created. If the userindicates that a new experiment should be created through the user input208, the experiment in the input 202 can be modified to create the newexperiment, such as by removing certain elements and consolidatingothers. This new experiment can be provided as output 210 where it canbe displayed to a user.

The following discussion now refers to a number of methods and methodacts that may be performed. Although the method acts may be discussed ina certain order or illustrated in a flow chart as occurring in aparticular order, no particular ordering is required unless specificallystated, or required because an act is dependent on another act beingcompleted prior to the act being performed.

Referring now to FIG. 3, a method 300 is illustrated. The method 300 maybe practiced in a computing environment and includes acts for creating ascoring experiment from a training experiment. The method includesidentifying elements of a training experiment (act 302). For example,various modules, such as those illustrated in FIG. 1A may be identified.

The method 300 further includes identifying consolidation andelimination of elements in the training experiment for a scoringexperiment (act 304). For example, in FIGS. 1C and 1D illustrate thatthe split module 112, the score module 118, and the evaluate modelmodule 120 can be identified for elimination FIGS. 1E and 1F illustrateconsolidation of the trained module 130, the algorithm module 104 andthe train module 114. Embodiments may identify these modules forelimination and consolidation.

The method 300 further includes determining that the user wishes to havea scoring experiment created from the training experiment (act 306). Forexample as illustrated in FIG. 1C, a prompt 122 is illustrated. When theuser interacts with the prompt, a determination can be made that theuser wishes to have a scoring experiment created from a trainingexperiment.

The method 300 further includes as a result, consolidating andeliminating the identified elements to create the scoring experiment(act 308). As discussed above, and as illustrated in FIGS. 1E and 1F,various modules can be identified and eliminated.

The method 300 may further include providing a graphical representationof the consolidation and elimination of identified elements. FIG. 1Fshows a graphical representation of a screen shot of a consolidationoperation. Thus, for example, embodiments may be implemented where thegraphical representation is an animation.

The method 300 may further include adding elements to the scoringexperiment for web service deployment. For example, FIG. 1E illustratesa web service input node 126 and a web service output node 128. Suchembodiments may automatically deploy the web service deployment with theadded elements.

Referring now to FIG. 4, a method 400 is illustrated. The method 400 maybe practiced in a computing environment. The method 400 includes actsfor automatically detecting and anticipating that an additional machinelearning experiment may be needed.

The method 400 includes, after successfully running a first experimentworkflow, automatically prompting a user that an additional experimentworkflow may be needed based on specific criteria associated with thefirst experiment workflow (act 402). For example, FIG. 1B illustratesrunning the training experiment 100 and FIG. 1C illustrates a prompt 122prompting the user.

The method 400 further includes receiving input from the user confirmingthe additional experiment workflow (act 404). For example, the user caninteract with the prompt 122 confirming that an addition experimentworkflow should be generated.

As a result of receiving input from the user confirming the additionalexperiment workflow, the method 400 further includes the systemautomatically reconfiguring the first experiment workflow, includingautomatically identifying all necessary modules for the additionalexperiment workflow and connecting them properly to perform the intendedsecond experiment workflow (act 406). As discussed above, FIG. 1Dillustrates the removal of certain elements and reconnecting of otherelements, and FIG. 1F illustrates the consolidation of certain elements.

The method 400 further includes displaying to the user the firstexperimental workflow transitioning from the first experiment workflowto the additional experiment workflow (act 408). Thus, for example, ananimation may be displayed with element floating off screen, elementsbeing combined, etc.

As discussed above, and illustrated in FIG. 1D, the method 400 may bepracticed where automatically reconfiguring the first experimentworkflow comprises identifying and removing irrelevant portions of thefirst experiment workflow.

The method 400 may be practiced where the first experiment workflow is atraining experiment workflow and where the additional experimentworkflow is a scoring experiment workflow.

The method 400 may be practiced where the specific criteria associatedwith the first experiment comprises the existence of both an algorithmand a training module. Thus, as in the example illustrated above, byidentifying that the training experiment includes both an algorithmmodule and a training module, it can be determined that a scoringexperiment may need to be created.

The method 400 may further include populating and creating elements forweb service deployment in the second experiment workflow. Thus, asillustrated in FIG. 1E, web service input and output nodes 126 and 128may be added.

The method 400 may further include receiving user input approving thesecond experiment workflow and as a result, automatically deploying theworkflow to a live web operation. Thus, as illustrated in FIG. 1L, auser may select the user interface element 144 to approve the secondexperiment.

The method 400 may further include displaying a user interface elementthat allows a user to toggle the additional experiment workflow betweenan online “live” connected mode and an offline “isolation” disconnectedmode. FIG. 1M illustrates a user interface element 146 that can be usedto toggle between the “live” connected (i.e. input and output nodesattached) and an offline “isolation” disconnected (i.e., with input andoutput nodes detached) modes.

The method 400 may be practiced where displaying to the user the firstexperimental workflow transitioning from the first experiment workflowto the additional experiment workflow comprises displaying an animationhighlighting the transition. Thus, as illustrated above, and inparticular in FIG. 1F and the corresponding description, embodiments mayanimate elimination and consolidation of elements.

The method 400 may further include displaying addition visual cuesindicating what has happened to transition the first experiment workflowto the additional experiment workflow. Various visual cues areillustrated in the Figures at 132, 134, 136, 138, 140, and 142. Thus,these visual cues illustrate where embodiments may further includefunctionality for identifying key differences and their respectivelocation on the user interface. In particular, by using the differentrepresentations of experiments, a user may be able to identifydifferences between experiments. In some embodiments, the identificationof differences may be automated by performing machine comparisons ofvarious experiments, or by keeping a log of changes performed to createone experiment from another. Embodiments may further includefunctionality for identifying key information to be conveyed based onthe detected differences and locating suitable data, such as the cues,explaining the detected differences. Embodiments may further includefunctionality for associating each explanation data in a near aproximity to each detected difference to be explained.

Embodiments may further include functionality for comparing “before” and“after” workflow data. For example, by using the tabs illustrated in theuser interface illustrated in the figures, a user can evaluate thebefore scenario of the training experiment 100 and the after scenario ofthe scoring experiment 124.

Further, the methods may be practiced by a computer system including oneor more processors and computer readable media such as computer memory.In particular, the computer memory may store computer executableinstructions that when executed by one or more processors cause variousfunctions to be performed, such as the acts recited in the embodiments.

Embodiments of the present invention may comprise or utilize a specialpurpose or general-purpose computer including computer hardware, asdiscussed in greater detail below. Embodiments within the scope of thepresent invention also include physical and other computer-readablemedia for carrying or storing computer-executable instructions and/ordata structures. Such computer-readable media can be any available mediathat can be accessed by a general purpose or special purpose computersystem. Computer-readable media that store computer-executableinstructions are physical storage media. Computer-readable media thatcarry computer-executable instructions are transmission media. Thus, byway of example, and not limitation, embodiments of the invention cancomprise at least two distinctly different kinds of computer-readablemedia: physical computer readable storage media and transmissioncomputer readable media.

Physical computer readable storage media includes LAM, ROM, EEPROM,CD-ROM or other optical disk storage (such as CDs, DVDs, etc), magneticdisk storage or other magnetic storage devices, or any other mediumwhich can be used to store desired program code means in the form ofcomputer-executable instructions or data structures and which can beaccessed by a general purpose or special purpose computer.

A “network” is defined as one or more data links that enable thetransport of electronic data between computer systems and/or modulesand/or other electronic devices. When information is transferred orprovided over a network or another communications connection (eitherhardwired, wireless, or a combination of hardwired or wireless) to acomputer, the computer properly views the connection as a transmissionmedium. Transmissions media can include a network and/or data linkswhich can be used to carry or desired program code means in the form ofcomputer-executable instructions or data structures and which can beaccessed by a general purpose or special purpose computer. Combinationsof the above are also included within the scope of computer-readablemedia.

Further, upon reaching various computer system components, program codemeans in the form of computer-executable instructions or data structurescan be transferred automatically from transmission computer readablemedia to physical computer readable storage media (or vice versa). Forexample, computer-executable instructions or data structures receivedover a network or data link can be buffered in RAM within a networkinterface module (e.g., a “NIC”), and then eventually transferred tocomputer system RAM and/or to less volatile computer readable physicalstorage media at a computer system. Thus, computer readable physicalstorage media can be included in computer system components that also(or even primarily) utilize transmission media.

Computer-executable instructions comprise, for example, instructions anddata which cause a general purpose computer, special purpose computer,or special purpose processing device to perform a certain function orgroup of functions. The computer executable instructions may be, forexample, binaries, intermediate format instructions such as assemblylanguage, or even source code. Although the subject matter has beendescribed in language specific to structural features and/ormethodological acts, it is to be understood that the subject matterdefined in the appended claims is not necessarily limited to thedescribed features or acts described above. Rather, the describedfeatures and acts are disclosed as example forms of implementing theclaims.

Those skilled in the art will appreciate that the invention may bepracticed in network computing environments with many types of computersystem configurations, including, personal computers, desktop computers,laptop computers, message processors, hand-held devices, multi-processorsystems, microprocessor-based or programmable consumer electronics,network PCs, minicomputers, mainframe computers, mobile telephones,PDAs, pagers, routers, switches, and the like. The invention may also bepracticed in distributed system environments where local and remotecomputer systems, which are linked (either by hardwired data links,wireless data links, or by a combination of hardwired and wireless datalinks) through a network, both perform tasks. In a distributed systemenvironment, program modules may be located in both local and remotememory storage devices.

Alternatively, or in addition, the functionally described herein can beperformed, at least in part, by one or more hardware logic components.For example, and without limitation, illustrative types of hardwarelogic components that can be used include Field-programmable Gate Arrays(FPGAs), Program-specific Integrated Circuits (ASICs), Program-specificStandard Products (ASSPs), System-on-a-chip systems (SOCs), ComplexProgrammable Logic Devices (CPLDs), etc.

The present invention may be embodied in other specific forms withoutdeparting from its spirit or characteristics. The described embodimentsare to be considered in all respects only as illustrative and notrestrictive. The scope of the invention is, therefore, indicated by theappended claims rather than by the foregoing description. All changeswhich come within the meaning and range of equivalency of the claims areto be embraced within their scope.

What is claimed is:
 1. In a computing environment, a system forautomatically detecting and anticipating that an additional machinelearning experiment may be needed, the system comprising: one or moreprocessors; and one or more computer-readable media, wherein the one ormore computer-readable media comprise computer-executable instructionsthat when executed by at least one of the one or more processors causethe system to perform the following: after successfully running a firstexperiment workflow, automatically prompting a user that an additionalexperiment workflow may be needed based on specific criteria associatedwith the first experiment workflow; receiving input from the userconfirming the additional experiment workflow; as a result of receivinginput from the user confirming the additional experiment workflow, thesystem using a consolidation module to automatically reconfigure thefirst experiment workflow, including automatically identifying allnecessary modules for the additional experiment workflow and connectingthem properly to perform the intended second experiment workflow; anddisplaying to the user the first experimental workflow transitioningfrom the first experiment workflow to the additional experimentworkflow.
 2. The system of claim 1 wherein automatically reconfiguringthe first experiment workflow comprises identifying and removingirrelevant portions of the first experiment workflow.
 3. The system ofclaim 1 wherein the first experiment workflow is a training experimentworkflow and wherein the additional experiment workflow is a scoringexperiment workflow.
 4. The system of claim 1 wherein the specificcriteria associated with the first experiment comprises the existence ofboth an algorithm and a training module.
 5. The system of claim 1wherein the one or more computer-readable media further comprisecomputer-executable instructions that when executed by at least one ofthe one or more processors cause the system to populate and createelements for web service deployment in the second experiment workflow.6. The system of claim 1 wherein the one or more computer-readable mediafurther comprise computer-executable instructions that when executed byat least one of the one or more processors cause the system to receiveuser input approving the second experiment workflow and as a result,automatically deploy the workflow to a live web operation.
 7. The systemof claim 1 wherein the one or more computer-readable media furthercomprise computer-executable instructions that when executed by at leastone of the one or more processors cause the system to display a userinterface element that allows a user to toggle the additional experimentworkflow between an connected mode and a disconnected mode.
 8. Thesystem of claim 1 wherein displaying to the user the first experimentalworkflow transitioning from the first experiment workflow to theadditional experiment workflow comprises displaying an animationhighlighting the transition.
 9. The system of claim 1 wherein the one ormore computer-readable media further comprise computer-executableinstructions that when executed by at least one of the one or moreprocessors cause the system to display addition visual cues indicatingwhat has happened to transition the first experiment workflow to theadditional experiment workflow.
 10. In a computing environment, a methodof automatically detecting and anticipating that an additional machinelearning experiment may be needed, the method comprising: aftersuccessfully running a first experiment workflow, automaticallyprompting a user that an additional experiment workflow may be neededbased on specific criteria associated with the first experimentworkflow; receiving input from the user confirming the additionalexperiment workflow; as a result of receiving input from the userconfirming the additional experiment workflow, the system using aconsolidation module to automatically reconfigure the first experimentworkflow, including automatically identifying all necessary modules forthe additional experiment workflow and connecting them properly toperform the intended second experiment workflow; and displaying to theuser the first experimental workflow transitioning from the firstexperiment workflow to the additional experiment workflow.
 11. Themethod of claim 10 wherein automatically reconfiguring the firstexperiment workflow comprises identifying and removing irrelevantportions of the first experiment workflow.
 12. The method of claim 10wherein the first experiment workflow is a training experiment workflowand wherein the additional experiment workflow is a scoring experimentworkflow.
 13. The method of claim 10 wherein the specific criteriaassociated with the first experiment comprises the existence of both analgorithm and a training module.
 14. The method of claim 10 furthercomprising populating and creating elements for web service deploymentin the second experiment workflow.
 15. The method of claim 10 furthercomprising receiving user input approving the second experiment workflowand as a result, automatically deploying the workflow to a live weboperation.
 16. In a computing environment, a system for creating ascoring experiment from a training experiment, the system comprising:one or more processors; and one or more computer-readable media, whereinthe one or more computer-readable media comprise computer-executableinstructions that when executed by at least one of the one or moreprocessors cause the system to perform the following: identifyingelements of a training experiment; identifying consolidation andelimination of elements in the training experiment for a scoringexperiment; determining that the user wishes to have a scoringexperiment created from the training experiment; and as a result, usinga consolidation module, consolidating and eliminating the identifiedelements to create the scoring experiment.
 17. The system of claim 16,wherein the one or more computer-readable media further comprisecomputer-executable instructions that when executed by at least one ofthe one or more processors cause the system to provide a graphicalrepresentation of the consolidation and elimination of identifiedelements.
 18. The system of claim 17, wherein the graphicalrepresentation is an animation.
 19. The system of claim 16, wherein theone or more computer-readable media further comprise computer-executableinstructions that when executed by at least one of the one or moreprocessors cause the system to add elements to the scoring experimentfor web service deployment.
 20. The system of claim 19 wherein the oneor more computer-readable media further comprise computer-executableinstructions that when executed by at least one of the one or moreprocessors cause the system to automatically deploy the web servicedeployment with the added elements.